Recently, with the development of life science field, micromanipulation technology has attracted attention. A two-fingered microhand system, that has been developed as a micro manipulator, can perform dexterous cell manipulation such as grasping, rotating, and transferring using two end effectors in a chopstick-like motion. The automated manipulation uses processing results based on all-in-focus (AIF) images and depth map obtained from the vision system; however, errors in the results still reduce the efficiency of the system. In this study, we introduce an automated grasping system that corrects the image processing error using the reaction force from the target object during grasping maneuvers. In our improved microhand system, micro force sensors that comprises strain gauge are attached to the two fingers of microhand. The two fingers with micro force sensor are set to be able to measure the horizontal and vertical forces to detect grasping state, precisely. Using the sensor information and the image processing results, we demonstrate that this system improves the accuracy and success rate of automated grasping.